Backdoor Attacks on Deep Learning Models in Mobile Apps
Mobile deep learning apps
Researchers discovered that numerous real-world mobile applications were vulnerable to backdoor attacks through neural payload injection. The empirical study covered critical applications such as face authentication and financial services.
- first report
- 3 Feb 2022
- last update
- 3 Feb 2022
Timeline · 2
Sources · 2
Research and catalogues
- 2022-02-03Backdoor Attack on Deep Learning Models in Mobile AppsMITRE ATLAS · atlas.mitre.org/studies/AML.CS0013
- 2022-02-03DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload InjectionarXiv · arxiv.org/abs/2101.06896
Titles and links belong to their publishers. The summary above is written by gcdRadar in its own words; data is CC BY-SA 4.0.
Classification
- lane
- Research
- AI role
- Attack on AI
- categories
- Supply chainAdversarial evasion
- severity
- 1 · Research / proof of concept
- targets
- Enterprise deployment
- owasp llm top 10
- LLM03:2025 Supply Chain
- mitre atlas
- AML.T0010 AI Supply Chain CompromiseAML.T0015 Evade AI ModelAML.T0043 Craft Adversarial Data
- dates
- happened 18 Jan 2021 · first reported 3 Feb 2022
- origin
- MITRE ATLASDerived from MITRE ATLAS (Apache 2.0, © MITRE).
- record
- confidence 0.95 · created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z
Lane, AI role, categories and severity come from the classifier; the OWASP and ATLAS rows are derived from the categories. How these are assigned
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the brief as text
# Backdoor Attacks on Deep Learning Models in Mobile Apps https://gcdradar.vercel.app/incidents/backdoor-attacks-on-deep-learning-models-in-mobile-apps · AIR-2022-0029 · closed 2022-02-17 - Lane: Research - Severity: 1 (Research / proof of concept) - Fix status: Fix status unknown - First reported: 2022-02-03 (happened 2021-01-18) - AI role: Attack on AI - Products: Mobile deep learning apps ## Summary Researchers discovered that numerous real-world mobile applications were vulnerable to backdoor attacks through neural payload injection. The empirical study covered critical applications such as face authentication and financial services. ## What to do No action recorded; read the sources below and the vendor's advisory. ## Latest updates - 2022-02-03: DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection (https://arxiv.org/abs/2101.06896) - 2022-02-03: Backdoor Attack on Deep Learning Models in Mobile Apps (https://atlas.mitre.org/studies/AML.CS0013) ## Sources - 2022-02-03 MITRE ATLAS: Backdoor Attack on Deep Learning Models in Mobile Apps https://atlas.mitre.org/studies/AML.CS0013 - 2022-02-03 arXiv: DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection https://arxiv.org/abs/2101.06896 Verify against the vendor's advisory before changing production. Data CC BY-SA 4.0, https://gcdradar.vercel.app.
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